Erschienen:
Springer Science and Business Media LLC, 2024
Erschienen in:
Journal of Food Measurement and Characterization, 18 (2024) 4, Seite 3008-3017
Sprache:
Englisch
DOI:
10.1007/s11694-024-02382-4
ISSN:
2193-4126;
2193-4134
Entstehung:
Anmerkungen:
Beschreibung:
AbstractThis study addressed pesticide contamination in beeswax, explicitly focusing on detecting Amitraz using innovative spectroscopy quantification. Beeswax’s susceptibility to pesticides poses risks to colony health, prompting the need for efficient detection methods. Current offline techniques, notably gas chromatography-mass spectrometry (GC-MS), are accurate but financially burdensome. Honeybees’ significance as global pollinators underscores the need to protect colonies from pesticides. Colony collapse disorder, triggered by pesticide exposure, is a widespread threat. This study proposes a cost-effective spectroscopy method for online hive monitoring, addressing gaps in existing identification methods. A systematic laboratory approach assessed the pesticide detection limit, targeting Amitraz in beeswax through online point spectroscopy and leveraging the short-wave infrared (SWIR) spectral range enhanced sensitivity to pesticide-induced color changes. The model, combining methods, proved reliable for estimating amitraz contamination, significantly exceeding one ppm, using Analytical Spectral Devices (ASD) sensors. Statistical analysis included. The study demonstrates the effectiveness of the AIW/BIW ratio in identifying amitraz concentrations above 1 ppm, particularly in the SWIR spectral range. Statistical analysis revealed a significant correlation between the AIW/BIW ratio and Amitraz concentrations, with a coefficient of determination (R2) of 0.9976. The proposed model, integrating methods, emerges as a dependable means for estimating amitraz contamination in beeswax, especially at concentrations exceeding 1 ppm, as validated through ASD sensors. Comparative analysis highlights the financial constraints associated with GC-MS and the impracticality of FTIR for online hive monitoring.